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, resilience and evolution of marine life to develop solid theories and predictive models of the relationships between marine biodiversity and ecosystem functions, which will in turn lead to improved economic
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regimes. This PhD project aims to develop predictive pore network models integrated with thermodynamics and upscaling methods toward reservoir-scale applications. We seek candidates with a strong background
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protein structural insight with hands‑on ML development: adapting and applying state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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during these experiments will be used to calibrate a numerical model of PFAS fate in soils. The predictions from this model will then be compared with PFAS concentration measurements in leachate collected
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learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
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based on Machine Learning (ML) emulators have taken the weather predictions research by storm, as they run faster and use less energy than traditional approaches: numerical models based on physical
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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—these approaches can recover unmeasured near-wall structures, improve subgrid-scale modelling, and enhance predictive accuracy. Possible project directions include: 1. Reconstructing near-wall velocity fields from
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, progression, and treatment outcomes. Skills in applying causal inference, survival analysis, and longitudinal modelling to link clinical and biological data. Expertise in predictive modelling and AI